Method and system for clustering optimization and applications

ABSTRACT

A computer-assisted method for evaluating a cluster assignment for an observation is disclosed. The disclosed method includes, for each of a plurality of observations, obtaining a data set containing no more than one proxy value for each of a plurality of variables, each variable having a plurality of possible values, the data set also containing a cluster assignment for the observation, the cluster assignment identifying one cluster from a plurality of clusters. The disclosed method also includes, for each observation from the plurality of observations, calculating a percent of proxy values for the plurality of variables that equals a mode of that observation&#39;s corresponding cluster&#39;s proxy values for the corresponding variables, and outputting the percent for each observation.

CROSS-REFERENCE TO RELATED APPLICATION

This application relates to, claims priority to, and incorporates by reference herein in its entirety, the following pending United States patent application:

-   -   Ser. No. 60/265,094, titled “Rosetta Methods”, filed Jan. 31,         2001.

This invention relates to and incorporates by reference herein in their entirety, the following pending United States patent applications:

-   -   Ser. No. 09/867,800, titled “Method and System for Clustering         Optimization and Applications”, filed 31 May 2001.     -   Ser. No. 09/867,804, titled “Method and System for Clustering         Optimization and Applications”, filed 31 May 2001.     -   Ser. No. 09/867,801, titled “Method and System for Clustering         Optimization and Applications”, filed 31 May 2001.     -   Ser. No. 09/867,802, titled “Method and System for Clustering         Optimization and Applications”, filed 31 May 2001.     -   Ser. No. 09/867,582, titled “Method and System for Clustering         Optimization and Applications”, filed 31 May 2001.

FIELD OF THE INVENTION

The present invention relates at least to the field of statistics, and, more particularly, to a method and system for clustering optimization and applications.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more readily understood through the following detailed description, with reference to the accompanying drawings, in which:

FIG. 1 is a flowchart of an exemplary embodiment of a method 1 of the present invention.

FIGS. 2 a, 2 b, and 2 c are a flowchart of an exemplary embodiment of a method 2 of the present invention.

FIG. 3 is a flowchart of an exemplary embodiment of a method 3 of the present invention.

FIG. 4 is a flowchart of an exemplary embodiment of a method 4 of the present invention.

FIGS. 5 a, 5 b, and 5 c are a flowchart of an exemplary embodiment of a method 5 of the present invention.

FIGS. 6 a and 6 b are a flowchart of an exemplary embodiment of a method 6 of the present invention.

FIG. 7 is a flowchart of an exemplary embodiment of a method 7 of the present invention.

FIG. 8 is a flowchart of an exemplary embodiment of a method 8 of the present invention.

FIG. 9 is a block diagram of an embodiment of an information device 9 of the present invention.

FIG. 10 is a flowchart of an exemplary embodiment of a method 10 of the present invention.

FIG. 11 is a chart for an actual blinded case study that plots survey variables against the percent of the surveyed population who agreed with a survey variable or indicated that the survey variable was important.

DETAILED DESCRIPTION Introduction

The combination of Wall Street pressure to accelerate earnings growth and an ever-increasing fragmentation of consumer preferences and media habits has created a strategic imperative for businesses to identify and understand a panoply of consumer product and service choices through marketing tools and frameworks, most notably customer segmentation.

Traditional approaches to segmentation have at least the following three fundamental flaws:

-   -   1. The segments are not truly distinct from one another against         a common set of variables. This lack of distinctiveness obscures         the resolution of each segment and the overall structure.     -   2. The needs and attitudes of a segment do not logically align         with purchase behaviors.     -   3. Each segment cannot be isolated and targeted from within the         general population.

Embodiments of methods and systems of the present invention can solve each of these three flaws through a unique and distinctive set of business processes and econometric modeling techniques and thereby provide businesses with the ability to create breakthrough marketing initiatives. Moreover, these inventive processes and techniques can be extended to solve numerous problems outside of the fields of marketing and business.

Why the Rosetta Inventive Method Works

Rosetta's inventive methodology combines two seemingly disparate disciplines, microeconomics and psychology, under the backdrop of econometrics.

Microeconomics teaches that purchase and sales behaviors are logical, rational, and predictable, but provides no insight into the customer beliefs that drive brand choice.

Psychology teaches that a finite set of personality types exist in a given population but provides no insight into the decision rules that lead to brand choice.

Econometrics provides the quantitative and analytic rigor needed to identify the finite sets of personalities each with unique and predictable purchase/sales behavior patterns and decision rules.

The Rosetta Segment-Based Marketing System combines and provides the benefits of the insights of economics and psychology in a quantitatively rigorous framework with rapidly actionable applications.

The Rosetta Difference

Rosetta's unique segmentation process, beginning with data collection and culminating in clustering optimization, advantageously defines segments that are collectively more differentiated and individually more homogeneous than those developed using previous industry best practices.

An example of these improved segments is shown in FIG. 11, which plots survey variables against the percent of the surveyed population who agreed with a survey variable or indicated that a survey variable was important. FIG. 11 reflects an actual blinded case study for a over-the-counter medication, and charts the Rosetta Segmentation Process against a K-means segmentation and against the responses of the total population. The responses have been normalized for similar variables with opposite wording/phrasing. In other words, for a variable in which the only possible responses are “agree”, “neutral”, and “disagree”, whereas if 30% of a particular population agrees with the statement, “I hate zebras” and 25% of that same population is neutral toward zebras, then, logically, the remaining 45% of that population must not hate zebras (i.e. 45% agree with the statement “I LIKE zebras”).

The solid line of FIG. 11 indicates the response distribution for the total population of individuals that responded to a survey on over-the-counter medication.

The dotted line indicates the response distribution for the same survey of a Rosetta segment whose constituents represent a sub-set of the total population. The heavy dashed line indicates the response distribution for the same survey of a k-means generated segment whose constituents represent a sub-set of the total population. All three lines were calculated using the same methodology. For each variable in the survey, the number of individuals in a group that responds “agree/important” to a variable is divided by the total number of individuals in the group that responded to the variable. In this way, the response patterns for the total population, a Rosetta segment, and a k-means segment can be graphically arrayed and evaluated.

An important criterion for evaluating a segmentation solution is determining whether response patterns for generated clusters are statistically different from the response pattern of the overall population of respondents. As call-out box 1 of FIG. 11 shows, the average calculated confidence that the Rosetta segment is statistically different from the overall population is 95%, while the k-means segment only has an average calculated confidence of statistical variance of 80%. This finding demonstrates that Rosetta's segmentation approach yields segments that are vastly more distinctive from the general population than k-means segments. This finding also necessarily reveals that Rosetta's segments have a higher degree of internal homogeneity (i.e. each Rosetta segment has a greater proportion of similarly responding observations than k-means segments).

Another important criterion for evaluating segmentation structures is logical consistency. In call-out box 2, the k-means segment is revealed to have a logical discrepancy. For variables that probe a respondent's reliance on the medical establishment (i.e. physicians, pharmacists, nurses), the k-means segment indicates that the segment agrees with being reliant on the medical establishment for treating illnesses. Logically, the same k-means segment should disagree with variables that indicate self-reliance for treating illnesses. However, the same k-means segment strongly agrees with the self-reliance for treating illnesses variables. The Rosetta segment is, in contrast, logically consistent because it agrees with being reliant upon the medical establishment for treating illnesses and disagrees (i.e. very few segment members agree) with being self-reliant for treating illnesses.

An additional advantage of the Rosetta segmentation approach is that more variables are revealed as informative regarding each segment's beliefs toward a particular concept. If a segment's response distribution for a set of variables closely mirrors that of the total population, the variables in question are generally dismissed as either non-discriminating (because there is no statistical variance from the overall population) or as price of entry (i.e. a concept to which so many individuals in the population respond identically that the concept is considered an existential to the market in question).

Rosetta's approach, however, reveals variables to be discriminating where other approaches, such as k-means clustering, fail. Call-out box 3 reveals how response to variables related to patient compliance with physician instructions (e.g. “get more bed rest”, “drink more fluids”, “take medication for 10 days”) are not statistically different from the total population for the k-means segment. These variables would normally be ignored as non-discriminating or dismissed as price-of-entry. Rosetta's approach reveals that these compliance variables are indeed highly discriminating in defining the segment. In addition, it makes sense logically for Rosetta's segment to agree with being reliant on the medical establishment for treatment AND to agree with variables relating to compliance. However, price-of-entry variables do exist in virtually all market categories (e.g. in carbonated sodas, “my soda needs to be carbonated”, is price-of-entry). A segmentation structure that discovers NO price-of-entry variables is suspect. Call-out box 4 is an example of where the Rosetta approach has indeed discovered some price-of entry variables related to product features.

Overview

The Rosetta Segment-based Marketing Process includes several inventive techniques that are disclosed herein. They include:

-   -   I. Babbitt Score     -   II. Bestfit Clustering     -   III. Champion/Challenger Clustering Refinement     -   IV. Composition Analysis     -   V. Segment-on-the-Fly     -   VI. Behavioral Segment Scoring     -   VII. Panel Analysis     -   VIII. The Overall Segment-Based Marketing Process

A description of marketing-oriented applications for each of these techniques follows, the description including background information, a brief problem statement, a brief solution statement, a discussion of the impact and/or other applications for the technique, and finally, a brief description of the methodology of the technique. Following these descriptions of these techniques as applied to marketing, a description of flowcharts showing exemplary embodiments of the techniques as applied more generally is provided.

Throughout these descriptions, several terms are used synonymously. For example, the term “segment” is synonymous with the term “cluster”, which is a grouping of similar observations. Likewise, the term “clustering structure” is a synonym for “clustering solution”, either of which are a finite set of clusters for a dataset, with, in some cases, constituents that are mutually exclusive and collectively exhaustive (“MECE”).

Also, the term “respondent” is synonymous with the term “observation”, which can be viewed as a record (or row) in a dataset for which values are recorded for a particular variable. Further, the term “question” is synonymous with the term “variable”, either of which can be viewed as a field (or column) in a dataset, the field having a recorded value for each observation. Moreover, the term “possible answer” is synonymous with the term “possible value”, and the term “provided answer” is synonymous with the term “provided value”.

I. Babbitt Score

A. Description

-   -   1. Background: One component of the overall clustering process,         described infra, is the survey piloting process. One objective         of the survey pilot is to identify the subset of questions         within a larger survey that are most discriminating. These         questions are then used in the full-enumeration study. The         current best practices involve identifying and removing         price-of-entry variables from the data collection process. A         price-of-entry variable is defined as a variable to which >80%         of the survey's population responds identically.     -   2. Business Complication: Although price-of-entry variables         generally consist of about ≦20% of a pilot's total questions,         this analysis does not eliminate enough questions from the data         collection process. Furthermore, variable analysis beyond         identifying price-of-entry can be a heuristic and arbitrary         process not grounded in empiricism. The concern is that         effective variables could be eliminated, and/or unproductive         variables could be retained.     -   3. Solution: The bulk of variables used in the data collection         process require either agree/neutral/disagree or important/no         opinion/unimportant responses in 5 or 7 point scales.         Statistical testing using standard techniques (e.g., chi-squared         analysis) of historical work by the inventors revealed that the         variables that were most predictive of segments had response         distributions of about 25% agree/important, 50% neutral/no         opinion, 25% disagree/unimportant.

B. Impact/Output

-   -   This technique provides a standardized method, rooted in         empiricism, for efficiently calculating a survey question's         potential effectiveness. Based on score magnitude a question is         either retained, subjected to additional analysis, or         eliminated.

Babbitt Score Evaluation ≦50 Keep 50 < x ≦ 100 Further Analysis >100 Eliminate

C. Methodology/Components

-   -   1. Group question responses (indicated here by numbers from 1-5         and 1-7) into either Top 2 Box or Top 3 Box:

Top 2 Box Response 5-Point Scale 7-Point Scale Description Agree/Important 1, 2 1, 2 Top 2 Box Neutral/No Opinion 3 3, 4, 5 Middle 1 or 3 Disagree/Unimportant 4, 5 6, 7 Bottom 2 Box

Top 3 Box Response 7-Point Scale Description Agree/Important 1, 2, 3 Top 3 Box Neutral/No Opinion 4 Middle Disagree/Unimportant 5, 6, 7 Bottom 3 Box

-   -   2. Calculate Response Distributions         -   a. # Agree/Important÷Total Population         -   b. # Neutral/No Opinion÷Total Population         -   c. # Disagree/Unimportant÷Total Population     -   3. Calculate Top Box Score

$\text{Top~~Box~~Score} = {{{\begin{matrix} \text{Top~~Box} \\ \text{Response} \\ \text{Percent} \end{matrix} + \begin{matrix} \begin{matrix} \text{Bottom~~Box} \\ \text{Response} \end{matrix} \\ \text{Percent} \end{matrix} - x}} \cdot 100}$

-   -   Where x=ideal response distribution for a “neutral/no opinion,”         the inventors have found that 50% works well     -   4. Calculate Difference Score

$\text{Difference~~Score} = {{{\begin{matrix} \text{Top~~Box} \\ \text{Response} \\ \text{Percent} \end{matrix} - \begin{matrix} \begin{matrix} \text{Bottom~~Box} \\ \text{Response} \end{matrix} \\ \text{Percent} \end{matrix}}} \cdot 100}$

-   -   5. Calculate Babbitt Score         Babbitt Score=Top Box Score+Difference Score     -   The Babbitt Score process steps are easily executed in a         spreadsheet or database and does not require any proprietary         software.         II. Bestfit Clustering

A. Description

-   -   1. Background: High-resolution segmentation can be considered to         be an important aspect of the inventors' business methodology.         Currently, the standard clustering approaches available in all         major statistical packages (e.g. SAS, SPSS, S-Plus) is the         k-means clustering algorithm, conjoint analysis, and         correspondence analysis.     -   2. Business Complication: Standard clustering approaches are         incompetent, because they achieve neither a reasonable level of         discrimination within a common set of variables across segments,         nor a reasonable level of homogeneity within each segment, which         the inventors call level of resolution. “Resolution” is defined         in part C of this section.     -   3. Solution: The inventors have developed a segmentation         methodology, Bestfit clustering, that accomplishes the 4         objectives listed in Section B2 (below). Bestfit clustering is a         segmentation algorithm that maximizes “fit” as the (weighted)         number of questions for which a respondent's answers correspond         to the mode of responses of that particular respondent's         segment.

B. Impact/Output

-   -   1. Bestfit clustering generates a segmentation solution that         within the specified constraints maximizes “fit”. These         constraints are:         -   Number of segments in final solution set (required)         -   Number of iterations (required)         -   Presence of an initial segmentation solution to be optimized             (optional)         -   Whether to conduct a systematic search (optional)         -   Whether to conduct thorough search (optional)         -   Variable weights (optional)     -   These components will be described in greater detail in the next         section of this description.     -   2. By maximizing “fit,” Bestfit clustering creates a         high-resolution segmentation solutions required to power the         inventors' business methodology. The inventors define         high-resolution segmentation as a clustering process that         accomplishes all of the following objectives:         -   a. Maximize inter-segment heterogeneity and discrimination         -   b. Maximize intra-segment homogeneity         -   c. Yield segments defined by logically connected beliefs         -   d. Yield segments whose beliefs are correlated with brand             choice

C. Examples

-   -   a. Inter-segment heterogeneity and discrimination: Segments in         the solution set are as distant from each other as possible         because response distributions are largely unique by segment         (e.g. doctor directed segment should be heavily “overdeveloped”         in comparison to the total population in agreeing with “I seek         medical advice” while a self-reliant segment should be very         “underdeveloped” in agreeing with that same belief statement).         The inventors use conventional “index” definitions and standards         in determining segment development versus the overall         population. An “index” is calculated on a response-specific         basis (i.e. calculated for agree, neutral and disagree).     -   The index is calculated by dividing the segment X response         percent for question Y by the total population response percent         for question Y, and multiplying the result by 100.     -   An index ≧120 is considered to be “overdeveloped” and an index         of ≦80 is considered to be “underdeveloped.”     -   b. Intra-segment homogeneity: Segments in the solution set must         be internally consistent. There are 2 standards the methods of         the present invention look for:         -   Non-conflicting responses (e.g. a doctor-directed segment             should agree with “I seek medical advice” and disagree with             “I don't trust doctors”).         -   Minimal bimodality in question-response distributions.     -   c. Logically connected beliefs: One way to logically define a         MECE (mutually exclusive, collectively exhaustive) segmentation         structure is to compare the constituent segments across a common         set (or stub) of variables. This comparison ensures that it is         possible to understand category, macro-segment, and sub-segment         dynamics through an inductive or deductive evaluation of         question-response distributions along common measures.     -   d. Beliefs correlated with brand choice: A segment with a         specific belief structure should have an overdevelopment in         consumption and/or usage of brand(s) whose current brand         equities are aligned with that segment's specific belief         structure. The primary measures of consumption and/or usage are:         brand penetration, brand share, and brand volume. Penetration is         a measure of how many individuals in a given population have         actually purchased and/or used a brand, product, or service         within a category. Share is a measure of a particular brand's         “ownership” (i.e. proportion) of the total category purchase         and/or usage as compared with competing brands. Volume is a         measure of how much a particular individual within a category         consumes of a specific brand, product, or service. The following         examples of each measure are for the analgesic category. It is         important to note that although the terminology used here is         CPG-oriented, analogues of these measures apply across         industries (including service-oriented industries such as retail         banking and technology industries such as computer equipment         manufacturers).         -   Penetration: A segment that does not believe in medicating             should have a significantly lower incidence of purchasing             and/or using analgesic medicines than a segment that             believes in aggressively medicating even the smallest             ailment.         -   Share: The same aggressive medicating segment should have a             much higher share of brands that contain the ingredients             that are known for performance efficacy (i.e. ibuprofen,             naproxen sodium). In contrast, a segment that is             safety-oriented should have a much higher share and usage of             ingredients known for their safety (e.g. acetaminophen).     -   Volume: A quantifiable measure of how much of a brand a consumer         purchases/uses in a given time period. Units of measure include         dollars spent and volumetric level (e.g. # of tablets, ml of         liquid). Variables that measure volume can be calculated in 2         ways: self-reported (i.e. survey respondent estimates volume)         and panel-derived (i.e. a survey respondent is a member of a         tracking panel such as IRI or AC Nielsen so his/her volume can         be calculated). For example, the same aggressive medicating         segment should spend more dollars and/or consume more pills         within the category and within brands aligned with its belief         structure than a non-medicating segment.

C. Methodology/Components

-   -   1. Fundamental Methodology         -   Let i (where i={1, . . . n}) denote each individual (i.e.             data observation) within the clustering population, and let             s(i) be the assigned cluster for i. If q(k,l) denotes i's             answer to question k (where k={1, . . . k}), then group the             data observations (i) into s segments (predefined             constraint) in order to maximize the following:

$\begin{matrix} {\underset{i = 1}{\sum\limits^{n}}{\underset{k = 1}{\sum\limits^{K}}{{{w(k)} \cdot 1}\left( {{q\left( {i,k} \right)} = {\arg\;{\max\limits_{j}\left\{ {\sum\limits_{{l\text{:}{s{(l)}}} = {s{(i)}}}{1\left( {{q\left( {l,k} \right)} = j} \right)}} \right\}}}} \right)}}} & (1) \end{matrix}$

-   -   Where 1(A) is an “indicator function” that equals 1 if A is         true, and 0 if A is false, and w(k) is the weight for question         k.

${\text{Note:~~}A} = \left( {{q\left( {i,k} \right)} = {\arg\;{\max\limits_{j}\left\{ {\sum\limits_{{l\text{:}{s{(l)}}} = {s{(i)}}}{1\left( {{q\left( {l,k} \right)} = j} \right)}} \right\}}}} \right)$

-   -   2. The initial segmentation solution (i.e. the starting point         that will be optimized in terms of (1) in successive iterations)         can be developed using 1 of the following 3 methods.         -   a. Use a pre-existing segmentation solution and group data             observations accordingly (this capability is the core of             technique III, Champion/Challenger Clustering Refinement).         -   b. Systematic search     -   For each pair of questions (k_(x)+k_(x+y)) the segmentation that         best describes those 2 questions using the specified number of         segments is found. To do this maximize:

$\overset{n}{\sum\limits_{i = 1}}\left\lbrack {{{{w\left( k_{x} \right)} \cdot 1}\left( {{q\left( {i,k_{x}} \right)} = {\text{arg}\;{\max\limits_{j}\left\{ {\sum\limits_{{l:{s{(l)}}} = {s{(i)}}}{1\left( {{q\left( {l,k_{x}} \right)} = j} \right)}} \right\}}}} \right)} + {{{w\left( k_{x + y} \right)} \cdot 1}\left( {{q\left( {i,k_{x + y}} \right)} = {\text{arg}\;{\max\limits_{j}\left\{ {\sum\limits_{{l:{s{(l)}}} = {s{(i)}}}{1\left( {{q\left( {l,k_{x + y}} \right)} = j} \right)}} \right\}}}} \right)}} \right\rbrack$

-   -   -   This will result in [K·(K−1)]÷2 segmentation solutions. The             fit defined in (1) is calculated for each segmentation             solution. The initial segmentation is the one that maximizes             (1).         -   c. Thorough Search: For each question, k, the segmentation             that best describes k is found. To execute this, maximize             for each k:

$\overset{n}{\sum\limits_{i = 1}}{{{w(k)} \cdot 1}\left( {{q\left( {i,k} \right)} = {\text{arg}\;{\max\limits_{j}\left\{ {\sum\limits_{{l:{s{(l)}}} = {s{(i)}}}{1\left( {{q\left( {l,k} \right)} = j} \right)}} \right\}}}} \right)}$

-   -   -   This will result in generating K segmentation solutions.             Then let k* denote the question that results in a             segmentation solution that maximizes (1). For each question             other than k*, the segmentation solution that best describes             that question and the k*-th question is found. Then             maximize:

${\overset{n}{\sum\limits_{i = 1}}{{{w\left( k^{*} \right)} \cdot 1}\left( {{q\left( {i,k^{*}} \right)} = {\text{arg}\;{\max\limits_{j}\left\{ {\sum\limits_{{l:{s{(l)}}} = {s{(i)}}}{1\left( {{q\left( {l,k^{*}} \right)} = j} \right)}} \right\}}}} \right)}} + {{{w(k)} \cdot 1}\left( {{q\left( {i,k} \right)} = {\text{arg}\;{\max\limits_{j}\left\{ {\sum\limits_{{l:{s{(l)}}} = {s{(i)}}}{1\left( {{q\left( {l,k} \right)} = j} \right)}} \right\}}}} \right)}$

-   -   Of the K segmentation solutions, the one that maximizes (1) is         used as the starting point for segmentation.     -   3. Once a segmentation solution is defined, an attempt to         improve “fit” is executed by:         -   a. Randomly change the segmentation for a random fraction,             θ, of the clustering data set         -   b. Then reassign segment membership for each data             observation (while keeping all other data observations at             their current segment assignments). Each time s(i) changes             for i, fit is calculated [cf. (1)]. This process is             continually repeated until changing s(i) for i (while             keeping all other data observations at their current             memberships) does not improve “fit” [cf. (1)]. The objective             is to find a segmentation solution whose “fit” cannot be             improved by reassigning only one data observation. Finding             such a solution constitutes 1 iteration.         -   c. If the fit of the solution discovered in the completed             iteration surpasses the fit of the segmentation solution             used to begin that iteration, the new solution is used as             the launching point for the next iteration.         -   d. The corollary of 3.c is true         -   e. θ is defined as P(θ≦x)=√{square root over (x)} for             xε(0,1)         -   This ensures that small values of θ are more likely than             large values.             This series of steps is easily executed using Fortran,             Gauss, SAS, or any other language with extensive             mathematical functionality.             III. Champion/Challenger Clustering Refinement

A. Description

-   -   1. Background: At a fundamental level, segmentation is an         attempt to increase the effectiveness of marketing strategy and         tactics by either reducing the number of unique marketing         targets from n (i.e., all individual customers/prospects) to a         manageable and actionable subset of finite marketing targets or         by increasing the number of targets from one group, in which all         customers/prospects are treated alike     -   2. Business Complication: The conventional approach to         clustering does not build segmentation structures cumulatively         (i.e. identifying the optimal solution by using a previous         “winning” segmentation as launch point for further clustering         analysis) but begins each new cycle of cluster analysis by         creating a new segmentation structure. This lack of analytic         continuity is problematic because developing the optimal         solution ends up being more a function of serendipity that the         result of a methodical and measured process leading to the         optimal solution.     -   3. Solution: The inventors have proven that there is a process         for making segmentation scheme evaluation and refinement more         systematic, efficient, and most importantly, more commercially         valuable to the marketer, as defined by the breakaway business         results achieved using the inventors' approach versus the         conventional approach.         -   a. The over-arching philosophy requires a Darwinian             evaluation process of segmentation solutions. Once a             “champion” emerges, it becomes the standard against which             subsequent analyses are to be evaluated. If one of these             “challengers” outperforms the current “champion” in any of             the 4 metrics discussed in section II and is not worse in             the remaining 3 metrics, that “challenger” then becomes the             new “champion.” This process is continued until no new             “challengers” win after 2 rounds of analysis subsequent to             the creation of the current “champion.”         -   b. The inventors' clustering process is predicated upon             “evolving” solutions superior to their progenitors. A             possible input to the inventors' clustering process is a             pre-defined segmentation solution that is used as the             analytic starting point.

B. Impact/Output

-   -   1. This invention creates a systematic process for evaluating         segmentation solutions. Although it cannot completely eliminate         the “art” aspect of the analytic process, it does force a         measure of discipline into the overall analytic process and an         evaluation standard rooted in empirical comparisons rather than         “intuition” and “guess-work.”     -   2. More importantly, this invention allows the user to refine a         “champion” segmentation solution by using that actual solution         as the starting point. As a result, the impact of data changes         (e.g. removing/adding data observations, weighting variables,         removing/adding variables) to that “champion” segmentation         scheme (i.e. improvement or degradation by the inventors' 4         standards) can be addressed with absolute certainty. Because         other segmentation methods do not allow for this “common         denominator,” it is impossible to empirically evaluate how a         segmentation structure has changed using those methods.     -   3. Finally, this invention reduces the time that needs to be         allocated to cluster analysis. If a particular dataset is         yielding good results but needs to be tested on more iterations,         rather than having to increase the total number of iterations,         the “champion” solution allows a “credit” of iterations that         reduces the time required. For example, if the stability of a         winning solution needs to be confirmed by running twice as many         iterations (to determine if the solution changes), this         technique allows a “credit” of iterations because the launch         point is the winning solution.

Cycle N Cycle N + 1 Conventional 1,000 iterations 3,000 iterations required Winning solution 3,000 iterations executed The inventors 1,000 iterations 3,000 iterations required Winning solution 2,000 iterations executed

C. Methodology/Components

The work steps required to execute this invention are identical to the Bestfit clustering process. The only difference is that rather than using a systematic or thorough process for determining a launching point, this technique builds on a previously identified solution.

IV. Composition Analysis

A. Description

-   -   1. Background: Any segmentation has the following caveats:         -   a. Did each individual answer the questions logically and             truthfully or did he or she respond             randomly/deceptively/disingenuously?         -   b. Was an individual assigned to his segment through             serendipity?         -   c. If an individual was not assigned to his segment through             serendipity, how representative (or aligned) with that             segment is he?         -   d. Is the final number of segments in the segmentation             structure the optimal number?     -   2. Business Complication: In order to maximize the effectiveness         of product positioning, advertising, media placement, and         promotions at the segment level, the marketer must be able to         conduct research that tests each of the aforementioned marketing         tactics within a group of segment members who exemplify the         segment. When executing this type of segment-based market         research, it is important to be able to eliminate from the         research process consumers who do not exemplify their assigned         segments. Moreover, because segmentation by definition requires         that all respondents in the analytic population be assigned to a         segment, the conventional approach does not differentiate         between segment members who truly exemplify their segments from         those who do not. This lack of clarity is one of the chief         obstacles preventing segmentation from progressing to actionable         and successful segment-based strategies and tactics.     -   3. Solution:         -   a. The inventors' invention is based on the fact that each             observation must be evaluated using the following criteria:             -   What fraction of questions answered by each respondent                 corresponds to the respective segment's response modes?             -   What is the probability that an individual is a                 “typical” member of this segment?             -   What is the probability that the individual belongs in                 each of the segments of the segmentation solution?         -   b. Based on these criteria, a segment member can be             classified into one of 3 groups:             -   Exemplars: An individual close to the core of a segment.                 This person has “high” scores for all 3 criteria                 discussed above             -   In-Betweeners: An individual “between” 2 or more                 segments. Generally the probabilities of being in those                 segments are comparable             -   Outliers: An individual who is not a “typical” member of                 his assigned segment and is also “between” 2 or more                 segments.     -   The specific ranges that mark each “group” vary widely by         category, industry, and even line of business. Therefore, it is         up to the judgment of the person conducting this analysis to         calibrate the results accordingly.

B. Impact/Output

-   -   This invention provides a quantitative method for efficiently         evaluating the “quality” of each segment member as described         above. Direct applications of this invention include:         -   a. Greater precision in creating gains charts, which are             used to define how deep on a target list a direct mail             campaign should be mailed         -   b. Superior market research (e.g. ability to observe             quintessential segment members in focus groups and recruit             them in qualitative research testing as described above)         -   c. The ability to remove people who “randomly” answer             surveys from the analytic population         -   d. An enhanced understanding of truly “distinct” segments             (e.g. a segment whose members are largely “In-Betweeners”             and/or “Outliers” is a less attractive marketing target             because it will be much harder to identify mathematically)         -   e. Evaluate the quality of the segmentation structure. If a             segment is heavily populated with in-betweeners, it is             probably a blend of 2 or more segments. This knowledge             enables an empirical decision rule for increasing/decreasing             the number of segments specified when clustering.

C. Methodology/Components

-   -   1. Establish Response-Question Probabilities         -   a. For a question, k that has L_(k) possible answers, the             probability (also known as “purposeful probability”) that             answer value l is selected by observations (e.g. survey             respondents) in segment m is estimated by

$\begin{matrix} {{{\hat{P}}_{m}\left( {k,l} \right)} = {{\frac{N_{m}\left( {k,l} \right)}{N_{m}}\left( {1 - {\delta \cdot L_{k}}} \right)} + \delta}} & (2) \end{matrix}$

-   -   -   where         -   N_(m)=total number of observations in segment m         -   N_(m)(k, l)=the number of observations in segment m that             gives the l-th answer to question k

$\delta = {\min\left\{ {0.02,\frac{1}{2L}} \right\}}$

-   -   -   b. If δ=0, then P_(m)(k, l) is the fraction of observations             in segment m that provides answer value l to question k. To             a certain extent, δ=0 is optimal but results in values that             are far too precise for any practical use. The value for 6             used by the inventors is a more conservative factor that was             derived through empirical experimentation.

    -   2. Execute the following statistical analyses:         -   a. Percentage of questions answered correctly:             -   This is quite simply the percent of questions answered                 by a specific respondent that corresponds to the                 response modes of that person's segment.         -   b. Probability-based Score:             -   This calculation helps determine if a given individual                 is a “typical” member of his/her segment. A person                 assigned to this segment who “purposefully” selected his                 responses should have probabilities described by                 P_(m)(k, l). A person assigned to this segment through                 serendipity (i.e. randomly answered a certain number of                 questions that happened to place that person into a                 particular segment) has probabilities (also known as                 “serendipity probabilities”) described by

$\frac{1}{L_{k}}.$

-   -   -   -   The score is calculated by taking the log of the ratio                 of:                 -   The probability of observing the answers actually in                     the dataset, if the probabilities of answering are                     P_(m)(k, l) to                 -   The probability of observing the answers actually in                     the dataset, if the probabilities of answers are

$\frac{1}{L_{k}}$

-   -   -   c. Segment membership probability score:             -   Suppose that P_(m)(k, l) truly represents the                 probability that an individual in segment m responds                 answer value l to question k             -   Suppose that (before the individual answers any                 questions) that s/he is equally likely to belong to each                 of the segments             -   Observing that individual's answers illuminates the                 probability that that individual belongs in each                 particular segment             -   Use the well-known Bayes' Theorem to calculate the                 probability of that individual belonging to each                 segment, conditional on his answers.                 -   Bayes' Theorem is:                 -   Let A₁, A₂ . . . , A_(k) be a collection of K                     mutually exclusive and exhaustive events with P                     (A1)>0 for i=1 . . . , K. Then for any other event B                     for which P(B)>0

${P\left( {A_{j}/B} \right)} = {\frac{P\left( {A_{j}\bigcap B} \right)}{P(B)} = \frac{{P\left( {B/A_{j}} \right)}{P\left( A_{j} \right)}}{\underset{i = 1}{\sum\limits^{K}}{{P\left( {B/A_{i}} \right)} \cdot {P\left( A_{i} \right)}}}}$

-   -   -   -   -    where j=1, . . . , K

            -   In this case, let Z_(ik) be the answer value that                 individual i gives to question k so that

${P\text{(Segment~~for~~individual~~}I} = {{\left. m \right|\text{answer)}} = \frac{\underset{k = 1}{\prod\limits^{K}}{{\hat{P}}_{m}\left( {k,Z_{ik}} \right)}}{\underset{j = 1}{\sum\limits^{M}}{\underset{k = 1}{\prod\limits^{K}}{{\hat{P}}_{j}\left( {k,Z_{ik}} \right)}}}}$

-   -   -   -   All 3 statistical analyses can easily be executed using                 any statistical software package or programming language                 with extensive mathematical functionality.

    -   3. From the results of the statistical analyses, each segment         member can be classified, and the quality of the segmentation         structure can be assessed.         V. Segment-on-the-Fly

A. Description

-   -   1. Background: In order to translate the insights derived from a         segmentation scheme into marketing initiatives, the marketer         must have a process to accurately identify and propel customers         and prospects into their correct segment. A number of techniques         exist for developing and executing this process (commonly known         as “typing tools”):         -   CHAID/CART (tree analysis)         -   Regression analysis         -   Fischer Discriminant Analysis         -   Neural networks     -   2. Business Complications: However, many marketing strategies         powered by segmentation often fail or yield disappointing         results because practitioners have not been able to accurately         “score” enough customers/prospects into their appropriate         segments. The primary root causes of failure are as follows:         -   Accuracy: Typing tools often misclassify customers/prospects             thereby rendering segment-based strategies and tactics             ineffective         -   Coverage: Typing tools accurately identify             customers/prospects but sacrifice breadth in order to             achieve that accuracy         -   Efficiency: Typing tools often require so many questions to             achieve sufficient accuracy that they are impractical             because customers/prospects will be unwilling to take the             time necessary to answer a lengthy set of questions         -   Flexibility: An inability to explicitly control the             trade-off between accuracy and coverage or vice versa.     -   3. Solution: The inventors have developed a process of question         reduction that, when applied to a high-resolution segmentation         structure, typically yields fewer than 15 questions that achieve         the following 4 criteria:         -   Small number of questions used         -   High level of marketing coverage for target segments meets             statistical/tactical objectives         -   High level of marketing accuracy for target segments meets             strategic/tactical objectives         -   Overall score for solution is ≧70%.     -   4. Definitions:         -   a. Marketing coverage: The percent of people in each actual             segment that are classified into the correct “predicted”             segment         -   b. Marketing accuracy: The percent of people in each             “predicted” segment that are actually in that specific             segment         -   c. Overall score: The percent of the entire analytic             population whose “predicted” segments are identical to their             actual segments

B. Impact/Output

-   -   1. The inventors' process for creating typing tools achieves a         level of accuracy and coverage that is superior to conventional         approaches     -   2. The inventors' process for developing segment-typing tools         allows the marketer to explicitly control the trade-off between         accuracy and coverage depending on the application by executing         the following:         -   a. To maximize marketing coverage of specific segments,             assign those segments weights >1         -   b. To maximize marketing accuracy of specific segments,             assign those segments weights <1.     -   3. The output of the inventors' scoring methodology consists of         an algorithm of constants and betas (i.e. coefficients of         regression). Unlike tree analysis, which involves tabulating         burdensome question pathways, this output can be efficiently         executed within a database or spreadsheet to score N customers.

C. Methodology/Components

-   -   1. Construct an approximation of the segmentation structure         (i.e., a “simpler” segmentation) using a subset of questions         based on a given set of K questions         -   a. Create a dummy variable for each segment so that if there             are M segments, then M variables are created for each             individual         -   b. Mathematically, execute the following process per             individual:

$Y_{im} = \left\{ \begin{matrix} {1\mspace{14mu}\text{if~~individual~~}i\text{~~belongs~~to~~segment~~}m} \\ {0\mspace{14mu}\text{otherwise}} \end{matrix} \right.$

-   -   -   c. Create a dummy variable for each answer to each question             so that if there are L_(k) possible answers to question k,             then L_(k) variables are created for that question per             individual         -   d. Mathematically execute the following process per             individual

$\chi_{ilk} = \left\{ \begin{matrix} {1\mspace{14mu}\text{if~~individual~~}i\text{~~gives~~the~~}l\text{-th~~answer~~to~~question~~}k} \\ {0\mspace{14mu}\text{otherwise}} \end{matrix} \right.$

-   -   -   e. For each segment m, Y_(im) is regressed using ordinary             least squares on {1,{x_(ilk):kεK,1≦l≦L_(k)−1}}

    -   This step will give a linear approximation to the probability         that a person with a particular set of answers to question set K         belongs to segment m         -   f. Calculate the approximation to the probability of             belonging to segment m for each individual         -   g. If segment weights are not used, the “simpler”             segmentation is now constructed by assigning each individual             to the segment that gives the highest value of the             approximation to the probability. The β's generated as part             of the output are the coefficients in the linear regression         -   h. If segment weights are used, an index is defined by             multiplying the approximation to the probability of             belonging to segment m by the weight associated with that             segment. The simpler segmentation is now constructed by             assigning each individual to the segment that gives the             highest index value. The β's generated as part of the output             are the coefficients in the linear regression

    -   2. Generate the question set for use in Part 1 (this is possible         using the steps outlined above)

    -   a. Use steps 1.a to 1.h to construct a simpler segmentation         based on only one question. This is done by searching through         all available variables in the data set and finding the one that         maximizes accuracy (i.e. the fraction of individuals whose         “predicted” segment assignments correspond to their actual         segment assignments)

    -   b. Once the simpler segmentation based on question M is         constructed, the (M+1)^(th) question is added by keeping         question M and searching the remaining questions for the         question that together with question M maximizes overall score.         This results in (M+1) questions

    -   c. Then execute a linear optimization to replace each of the         (M+1) questions with each of the questions in set K that was not         included. This process should be continued until it is no longer         possible to improve coverage by replacing any one of the         questions and leaving the remaining unchanged; this process         gives the questions to be used when segmentation is done based         on (M+1) questions.         This entire process is executable using a mathematical         programming language such as Fortran, Gauss, or statistical         packages in tandem with C++ or other languages in which a linear         optimization can be programmed.         VI. Behavioral Segment Scoring

A. Description

-   -   1. Background: Database and data capture technologies have         advanced to such a point that many industries track         customer-level behaviors (e.g. financial services, retail sales,         travel). A number of data mining techniques have been developed         whose intent is to deduce customer habits by analyzing their         behaviors (e.g. collaborative filtering).     -   2. Business Complication: Unfortunately, behaviors are not         necessarily indicative of customer beliefs. Consequently, those         analytic systems have at best achieved modest success in         designing tailored marketing strategies and tactics.     -   3. Solution: Because the inventors' approach to segmentation is         comprised on a comprehensive set of needs, attitudinal and         behavioral variables to generate discrete, high-resolution         segments, whose beliefs drive discrete behavioral patterns,         detailed behavioral variables can be combined using a         proprietary modeling technique to generate an accurate and         scalable typing tool. The inventors have developed a method for         efficiently leveraging behavioral databases to understand         customer behavior. This application of Segment-on-the-Fly^(SM)is         most successful in industries that track rich behavioral data at         the customer level (e.g. credit card, retail sales, grocery         stores, travel companies).

B. Impact/Output

-   -   1. The ability to use customer-level behaviors to rapidly type         individuals into belief-based segments is highly scalable         because no dialogue with the customer is required (to generate         responses to the Segment-on-the-Fly^(SM) questions.     -   2. As with the Segment-on-the-Fly^(SM) typing tool described in         chapter V of this document, a scoring algorithm that can be         calibrated by weighting segments to optimize either marketing         coverage or accuracy.

C. Methodology/Components

-   -   1. Data Conversion         -   a. Determine optimal distribution of behaviors and sort into             a finite number of groups         -   b. Generally, a normal distribution, with each group having             a statistically significant number of individuals, yields             the best results. This analysis can be executed in any             database or spreadsheet.         -   c. Behaviors, especially dollar values and other such             continuous values, must be grouped into categorical values             in order to create sufficient commonalities within the data             set to enable clustering.     -   2. Variable Reduction     -   Ideally, all of the variables would be used in the         Segment-on-the-Fly^(SM) process. In the event the number of         variables is unwieldy (i.e. >100), it is acceptable to use         CHAID/CART or factor analysis to reduce the variable set to         <=100. The reason for doing this is that the linear optimization         phase of developing the typing tool becomes impractical with an         extremely large data set because the number of iterations         required to cycle through the question combinations increases         exponentially. Segment assignments are to be used as the         objective function.     -   As with Segment-on-the-Fly^(SM) proper, this process can be         executed within a mathematical programming language such as         Fortran or statistical packages in tandem with C++ or other         languages in which linear optimization can be programmed.     -   3. Execute Segment-on-the-Fly^(SM)     -   Once the variable set has been reduced to a realistic size,         execute the same exact steps as described in Section V.     -   As with Segment-on-the-Fly^(SM) proper, this process can be         executed within a mathematical programming language such as         Fortran or a statistical package in tandem with C++ or other         language in which linear optimization can be programmed.         VII. Panel Analysis

A. Description

-   -   1. Background: A number of market research companies maintain         panels of customers that are dedicated to studying customer         behaviors within a specific channel (e.g. Internet),         category/industry (e.g. consumer packaged goods), or behavioral         pattern (e.g. media consumption). Most companies use these         panels to obtain a better understanding of their competitive         markets. Current best practices in using these panels involve         using analyses of demographics and consumption levels to divine         the drivers of consumer demand. This approach, in essence, tries         to understand demand-drivers through the lens of supply-side         analytics.     -   2. Business Complication: As a result, the only credible         application of a supply-side panel analysis is to understand         macroeconomic trends in a given category/industry. However,         attempts at using panels to conduct demand-side (i.e. consumer         beliefs) analysis have gone awry because behaviors frequently do         not reflect consumer beliefs.     -   3. Solution: The inventors have developed a procedure for         conducting rigorous, demand-characterizing segmentation through         the proprietary process described earlier in this document. The         inventors' proprietary approach is not restricted to a specific         channel, industry, or behavioral type. The inventors use a         series of panels that track actual category usage and brand         choice behaviors at either the household or individual level in         the following 2 ways:         -   a. As the source of objectively captured behavioral             variables as the inputs to the critical behavioral variables             used in the inventors' segmentation process         -   b. A method for tracking changes in segment market share,             category usage, and penetration as well as their causal             marketing drivers (e.g. promotions, advertising, new product             launch, etc. . . . )

B. Impact/Output

-   -   Regardless of panel-type, the impact of this process is highly         material, to both developing high-resolution segmentation         structures and monitoring/refining segment-based strategies and         tactics:     -   1. Objective inputs to behavioral variables (measured in         continuous values such as dollars or actual volumetric         consumption):         -   a. Overall category usage (i.e. gross-up of all             category-relevant items)         -   b. Category and/or brand penetration (e.g. how many             individuals within the population use/purchase the category             and/or brand in question)         -   c. Brand share (e.g. a particular brand's share of category             purchase/usage)         -   d. Category and/or brand volume (e.g. a quantifiable amount             of a category and/or brand that individuals in a given             population use/purchase)         -   e. Ingredient composition (e.g. acetaminophen, multi-grain,             cholesterol free)         -   f. Form (e.g. liquid, solid, crunchy)         -   g. Company-level (i.e. gross-up of a company's portfolio of             brands in the category)         -   h. Individual brands (e.g. Tylenol, Nestle Crunch, Diet             Pepsi)     -   2. Segment-based tracking applications         -   a. Segment-level consumption/share         -   b. Beliefs that drive purchase decisions         -   c. Correlations among segments and brand choice         -   d. Alignment (or lack thereof) of segment-beliefs with brand             equities         -   e. Segment-level economics         -   f. Segment-based media planning         -   g. Measuring advertising effectiveness             -   i. Message performance: determine if advertising message                 (i.e. copy) had an influence, positive or negative, on                 brand awareness, penetration, share, and volume.             -   iii. Media performance: determine if advertising media                 (i.e. vehicle such as print or television) had an                 influence, positive or negative, on brand awareness,                 penetration, share, and volume.

C. Methodology/Components

-   -   Many of the details below are identical to those outlined in         previous sections of this document:     -   1. Refine survey by using Babbitt score (Section I).     -   2. Develop demand-side understanding of a given market by using         Bestfit clustering to segment the data that was collected using         the survey refined in Step 1.     -   3. Use composition analysis to group segment-members into         Exemplars^(SM), In-Betweeners^(SM), or Outliers^(SM) and         evaluate composition scores.     -   4. Use Segment-on-the Fly^(SM) to develop a typing tool for use         in the survey panel     -   5. Score a representative sample of the survey panel using a         typing tool. The channel for fielding this survey can be         selected in accordance with specific objectives and/or         constraints.     -   6. Conduct segment-level analysis to complete one or more of the         applications listed in technique 7, section VII.C.     -   7. Conduct sub-segment analysis among identified Exemplars^(SM),         In-Betweeners^(SM) and Outliers^(SM) in order to refine analyses         executed in the previous process step.         VIII. The Overall Segment-Based Marketing Process

A. Description

-   -   1. Background: The combination of Wall Street pressure to         accelerate earnings growth and an ever-increasing fragmentation         of consumer preferences and media habits has created the         financial imperative for every marketing-driven business to         focus its strategy and tactics against the highest potential         customer/prospect targets. Segmentation is the conventional         marketing tool to select and profile the target.     -   2. Business Complication: Unfortunately, traditional approaches         to segmentation have the following 3 fundamental flaws:         -   a. The segments are not truly distinct from one another             against a common set of variables. This lack of             distinctiveness obscures the resolution of each segment and             the overall structure         -   b. The needs and attitudes of a given segment do not             logically align with purchase usage and behaviors         -   c. Each segment cannot be isolated and targeted from within             the general population     -   3. Solution: The inventors' Segment-Based Marketing System^(SM)         is a business methodology that has solved each of these 3 flaws         through a unique and distinctive set of business processes and         econometric modeling techniques. The inventors' process provides         businesses with the ability to create breakthrough marketing         initiatives that have been proven to achieve profitable revenue         growth that exceeds traditional approaches to marketing.

B. Impact/Output

-   -   1. Marketing Strategy and Tactics         -   By using the inventors' Segment-Based Marketing System^(SM),             companies, regardless of industry and line of business, can             re-design their marketing strategies and tactics in the             following areas:         -   a. Advertising, which includes:             -   Copy strategy             -   Development of creative             -   Quantitative copy effectiveness testing         -   b. Positioning, which includes:             -   Brand equity             -   Attribute association             -   Benefit statements         -   c. Media, which includes:             -   Planning/purchasing             -   Media vehicle selection             -   Media vehicle evaluation             -   CPM* optimization         -   d. New product development             -   Need gap analysis             -   Price-attribute-bundle optimization             -   Positioning (see above)         -   e. Promotion, which includes:             -   Customer relationship management (CRM)             -   Sales force optimization             -   New Product launch         -   f. Tracking/refinement, which includes:             -   Campaign management and evaluation             -   Database design and management             -   Monitoring share and usage by target             -   Segment-level economics     -   *CPM—Cost per Thousand (impressions)     -   2. Business Valuation/Performance Management     -   In addition, because a company's portfolio of customers has a         direct and material impact on its profitability and growth         potential, the inventors' Segment-Based Marketing System^(SM) is         particularly germane to the following activities:         -   a. Private equity/venture capital             -   Understanding a potential investment target's customer                 mix would enable PE/VC firms to develop a quantitative                 understanding of their investments' present and future                 cash flows.         -   b. Leveraged buy-out             -   LBO shops could determine how attractive a potential                 take-over target is and identify the strategies and                 tactics needed to “repair” it.         -   c. Investment banking             -   Corporate finance: Leverage understanding of a client's                 customer base (and therefore drivers of cash flow) to                 improve decision rules for valuation and                 capital-raising.             -   Mergers and acquisitions: Determine synergy of customer                 portfolios of the merging companies and/or calculate the                 true value of an acquisition target's brand equity (i.e.                 goodwill) and customer base.             -   Equity research: Enhance the understanding of specific                 company and industry profit/loss drivers.

C. Methodology/Components

-   -   1. Developing Deep Customer/Prospect Insights         -   A marketing-driven company can implement the overall             business methodology by uniquely combining the inventions             disclosed herein with standard marketing techniques.     -   2. Marketing Strategies and Tactics         -   The deep customer/prospect insights gleaned through high             resolution segmentation can be translated into actionable             marketing programs. The inventors' proprietary methods for             typing customers/prospects are the means by which these             insights are executed in different business applications.             The following chart provides several examples of             Segment-Based Marketing applications. It is not intended to             be a complete and exhaustive list of applications.

D. Data Types

Some of the disclosed processes use scalar, categorical, and/or continuous variables from surveys fielded to a study population and/or behavioral/purchase data obtained from either the business itself or a panel, such as IRI, AC Nielsen, comScore, Simmons, MRI, or Nielsen ratings panel (not affiliated with AC Nielsen).

Data Input Data Type Accessibility Needs Scalar or Categorical Common; used by most Variables industry firms Attitude Scalar or Categorical Common; used by most Variables industry firms Behavior Scalar or Categorical Common; used by most Variables or Continuous industry firms transformed into Categorical/Scalar Panel-Derived Categorical or Uncommon; requires Variables Continuous subscription or alliance with a transformed into panel company with IRI or Categorical/Scalar Nielsen. The inventors have relationships with IRI and comSCORE Business-Derived Categorical or Common; used by most Variables Continuous industry firms transformed into Categorical/Scalar Composite Categorical or Rare; created by the inventors Variables Continuous using database-derived and/or transformed into panel-derived variables Categorical/Scalar

DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart of an exemplary embodiment of a Method 1 of the present invention. All and/or part of Method 1 can also be known as the Babbitt Score technique.

At activity 1100, for all observations (respondents), provided values (e.g., responses) associated with a variable (e.g., survey question), can be converted into proxy values, if necessary to insure that each variable has only a finite set of values to be analyzed. For example, if the provided values are continuous, those provided values can be converted into one of several scalar/categorical or discrete proxy values. By way of further example, if a variable was “On a scale of 1 to 10 (with 10 being best), how do you rate the service you received?”, and a provided value was 8.2, that provided value could be converted to a proxy value of 8. Furthermore, proxy values can be a subset of provided values. For example, if a provided value was categorical (e.g., “red”, “green”, or “blue”), then the proxy values can also be categorical, and can be identical to the provided values. Moreover, the conversion of provided values to proxy values explained in relation to activity 1100 applies to the provided values of Methods 2 through 8 and 10 as well (described below).

At activity 1200, proxy values associated with a variable (e.g., survey question) can be segregated into categories, (e.g., Top 2 box, Top 3 box, etc.). At activity 1300, for each category, a response distribution can be determined by dividing a number of responses in the category by the total number of corresponding responses for all categories. At activity 1400, a top box score can be calculated by adding the top box response (%) to the bottom box response (%) and subtracting an ideal distribution of neutrals (%) to obtain a result. Then, an absolute value of the result can be multiplied by 100.

At activity 1500, a difference score can be calculated by subtracting the bottom box response (%) from the top box response (%) and multiplying an absolute value of the result by 100. At activity 1600, an effectiveness score, also known as the Babbitt score, for the survey questions can be calculated by adding the top box score to the difference score. At activity 1700, the survey question can be evaluated based on the effectiveness score.

FIGS. 2 a, 2 b and 2 c are a flowchart an embodiment of a method 2 of the present invention. All and/or part of Method 2 can also be known as the Bestfit Clustering technique. Referring to FIG. 2 a, at activity 2100, for each observation, a dataset can be obtained, each dataset having observation identifications, variables, possible values, and provided values (where applicable provided values include any developed proxy values). At activity 2200, a number of clusters can be specified, any number of desired variables' weights for any number of desired variables within a data set, and a maximum number of iterations of the clustering solution can be specified. The number of clusters can be an integer greater than zero, and the number of iterations can be an integer greater than zero. At activity 2300, initial cluster assignments can be developed. These initial cluster assignments can be developed using any of three techniques.

The first technique can be shown at activity 2350, where a specified initial cluster assignment can be obtained from a previous dataset.

The second technique can be shown at activities 2360 through 2362, where a systematic search can be made for the initial cluster assignment. In this systematic search, a pair of variables can be identified that creates a clustering solution that maximizes fitness score using the specified number of clusters. At activity 2361, the one variable that creates a clustering solution that maximizes score using the specified number of cluster can be discovered. At activity 2362, the discovered variable from activity 2361 can be held constant and a second variable that creates a clustering solution that maximizes fitness score using the specified number of clusters can be discovered.

The third technique can be shown at activities 2370 through 2373, where a thorough search can be made. During the thorough search, any two variables that together create a clustering solution that maximizes fitness score using the specified number of clusters can be identified. At activity 2371, the one variable that creates a clustering solution that maximizes fitness score using specified number of clusters can be discovered. At activity 2372, holding the discovered variable from activity 2371 constant, a second variable that in tandem with the variable being held constants creates a clustering solution that maximizes fitness score using a specified number of clusters can be discovered. At activity 2373, the second discovered variable can be held constant and a third variable can be found that in tandem with the second variable being held constant creates a clustering solution that maximizes fitness score. Activities 2372 and 2373 can be repeated interactively and iteratively by cycling through all possible combinations of variable pairings until the fitness score that can be calculated in activity 2373 can be maximized.

After one of the three techniques are followed for developing an initial cluster assignment, at activity 2400, the observation identifications, the cluster identifications, and the fitness scores can be stored.

Referring to FIG. 2 b, at activity 2450, for each cluster and variable, the mode of the provided values can be calculated. At activity 2460, the cluster identification, variable identification, and corresponding mode of activity 2450 can be stored. At activity 2470, a fraction of observations can be selected and divided evenly among all specified clusters for the dataset. Preferably, the observations are chosen randomly and the exact fraction does not exceed 10% of the entire dataset's population of observations. At activity 2480, the observations from activity 2470 can be randomly reassigned to different clusters. At activity 2490, for each cluster and variable the mode of the provided values can be calculated. At activity 2500, a fitness score to the clustering solution developed in activity 2480 can be calculated. At activity 2505, the fitness score, cluster assignments and observation identifications can be stored.

At activity 2510, one random observation from a random cluster can be selected and its cluster assignment can be changed. At activity 2520, the fitness scores of activity 2510 can be calculated. At activity 2530, the fitness score of activity 2520 can be compared with the fitness score of activity 2505. At activity 2531, if the fitness score of activity 2520 can be less than or equal to the fitness score of activity 2505, all possible cluster assignments for the observation selected in activity 2510 can be cycled through until the fitness can be maximized.

At activity 2532, the maximum fitness score of activity 2531 can be compared with that of 2505. At activity 2533, if the maximum fitness score of activity 2531 can be less than or equal to the fitness score of activity 2505, then the selected observation can be returned to the original cluster assignment. At activity 2534, method 2 returns to activity 2510.

Considering again the comparison at activity 2532, if the maximum fitness score of activity 2531 can be greater than the fitness score of activity 2505, at activity 2535 method 2 proceeds to activity 2540. At activity 2540, the observation identification can be stored as well as the new cluster assignments. At activity 2550, the cluster assignments of activity 2505 are replaced with the cluster assignments of activity 2540 and method 2 returns to activity 2510.

Considering again the comparison at activity 2530, if the fitness score of activity 2520 can be greater than the fitness score of activity 2505, method 2 proceeds to activity 2540. If method 2 can be looping from activity 2550, once the fitness score of activity 2520 can be compared with that of activity 2505 at activity 2530, method 2 then proceeds to activity 2551. At activity 2551, if the fitness score of activity 2520 can be equal to that of activity 2505, method 2 proceeds to activity 2560. At activity 2552, if the fitness score of activity 2520 can be less than or greater than that of activity 2505, method 2 returns to activity 2540.

Referring now to FIG. 2 c, at activity 2560, the iteration ends, where activities 2510-2560 represent one iteration. At activity 2570, the iteration identification, the cluster identification, and the corresponding cluster assignments are stored. The iteration identification can be a positive integer that can be less than or equal to the total number of specified iterations. The value of the iteration identification can increase serially in the form of x+1 where x equals the previous iteration's identification.

At activity 2580, the iteration identification from activity 2570 can be compared with the total number of iterations specified in activity 2200. At activity 2581, if the iteration identification from activity 2580 can be less than the total number of iterations specified in activity 2200, method 2 can return to activity 2470. At activity 2582, if the iteration identification from activity 2580 can be equal to the total number of iterations specified in activity 2200, method 2 can proceed to activity 2600. At activity 2600, the iteration identification that produced the maximum fitness score, the maximum fitness score itself, the observation identifications, and the corresponding cluster assignments can be stored. At activity 2610, the data from activity 2600 can be placed in a file, such as an ASCII, .csv, .txt, or .prn file. At activity 2620, method 2 can be terminated.

FIG. 3 shows an exemplary embodiment of a method 3 of the present invention. All and/or part of Method 3 can also be known as the champion/challenger clustering refinement technique. At activity 3010, for each observation, a dataset can be obtained, the dataset having variables, possible values, and provided values. At activity 3020, initial cluster assignments are appended to the dataset so that those initial cluster assignments corresponds to observation identifications. At activity 3030, a maximum number of iterations can be specified. At activity 3040, activities 2450-2610 of method 2 can be executed. At activity 3050, method 3 can be terminated.

FIG. 4 is a flow chart of an exemplary embodiment of a Method 4 of the present invention. All and/or part of Method 4 can also be known as the composition analysis technique.

At activity 4010, for each observation, a dataset is obtained having a cluster assignment for the observation and having a proxy value for each of the variables in the dataset, each variable having possible values. At activity 4020, for each observation, an estimate is made that a purposeful probability (a measure of a probability that an observation in a particular cluster provides an answer to a question in a non-random manner) that a particular possible value for a particular variable will be provided by observations assigned to a particular cluster. At activity 4021, probability variables for each cluster, variable and answer combinations are created as P_(m)(k, l). At activity 4022, the probability that answer value l is given by the observations in cluster m for variable k that has L_(k) possible answers is estimated. At activity 4023, a value δ is defined within a constraint that allows for usable output. The value

$\delta = {\min\left\{ {0.02,\frac{1}{2L}} \right\}}$ is a value that produces meaningful results. If δ=0, the resulting over-precision of the calculated probabilities can compromise computational efficiency.

At activity 4024, a computational process is executed across all

${{P_{m}\left( {k,l} \right)} = {{\frac{N_{m}\left( {k,l} \right)}{N_{m}}\left( {1 - {\delta \cdot L_{k}}} \right)} + \delta}},$ where N_(m)=the total number of observations in cluster m; N_(m)(k,l)=the number of observations in cluster m who give the l-th answer value to variable k; and

$\delta = {\min{\left\{ {0.02,\frac{1}{2L}} \right\}.}}$

At activity 4025, for each observation, the purposeful probability can be stored and/or outputted. At activity 4030, for each observation and each possible value, a serendipity probability (a measure of a probability that a observation in a particular cluster will be associated with any of the possible values for a particular variable) can be calculated. If an observation i in cluster m selected responses “randomly”, then the probabilities of selecting his responses should be described as

$\frac{1}{L_{k}}.$

At activity 4035, for each observation, a ratio of purposeful probability to serendipity probability can be calculated. At activity 4040, for each observation, a logarithm of the ratio from activity 4040 can be calculated to obtain a composition analysis score. At activity 4045, for each observation, the composition analysis score can be stored and/or outputted.

At activity 4050, for each observation, an assumption can be made that before an observation is made, the observation has an equal probability of being in any cluster. At activity 4055, for each observation, an assumption can be made that the purposeful probabilities are true. Thus, if observation i in cluster m purposefully and logically selected his responses, then the probabilities of selecting his responses should be described by P_(m)(k,l). At activity 4060, for each observation, a Bayes probability can be calculated that a particular observation can be in each cluster is conditional upon the observation s proxy value. At activity 4065, for each observation, the Bayes probability can be stored and/or outputted.

At activity 4080, for each observation, a percent of proxy values for the variables that equals a modes of that observation s cluster s proxy values for the corresponding variables can be calculated. At activity 4085, for each observation, the calculated percent can be stored or outputted.

At activity 4090, each observation can be classified based on the results obtained in activity 4045, 4065 and/or 4085.

FIG. 5 a shows an exemplary embodiment of a method 5 of the present invention. All and/or part of Method 5 can also be known as the Segmentation-On-The-Fly technique.

At activity 5100, a dataset for observations can be obtained, the dataset having variables, possible values, provided values, and corresponding cluster assignments. For the purposes of FIG. 5 a the total set of variables can be defined as {K} and the maximum number of variables to be used can be defined as k_(max)(x). At activity 5200, a determination can be made regarding whether cluster weights are needed to meet coverage or efficiency objectives. Underweighting can be used to implement an efficiency objective, while overweighting can be used to implement a coverage objective.

At activity 5210, if no weights are needed, method 5 can proceed to activity 5300. At activity 5220, if weighting can be needed, each cluster can be assigned a weight using the decision rules of one of activities 5221, 5222, or 5223. In the weighted situation, it can be preferable to assign weights to all clusters regardless of the magnitude of the clusters. In activity 5221, if the cluster can be unweighted, the weight can be set as w=1. At activity 5222, if the cluster can be to be overweighted, the weight can be set as w>1. At activity 5223, if the cluster can be to be underweighted, the weight can be set as 0<w<1.

At activity 5300, the clustering solution developed using Method 2 can begin to be approximated by developing a clustering solution that employs only 1 variable from {K} in each. In other words if there are K variables, then K optimized solutions can be created 1 per k in {K}. An optimized solution is a clustering solution that has achieved a maximum score (however defined) within its defined constraints (e.g. number of variables, number of respondents, number of iterations, number of clusters, etc.) At activity 5310, a dummy variable can be created for each cluster so that if there were M clusters, then there are M variables. For example, let Y_(im) designate a dummy variable for observation i in cluster m. Let i be a member of the set of observations {R}, k be a member of the set of variables {K}, and m be a member of the set of clusters {M}. At activity 5320, m variables can be populated per observation per clustering solution. At activity 5321, if observation i can be in cluster m then Y_(im) can be set to 1. At activity 5322, if observation i can be not in cluster m then Y_(im) can be set to 0. At activity 5330, all values for M variables can be stored for all observation for each clustering solutions.

Referring to FIG. 5 b, at activity 5340 a dummy variable can be created for every variable-possible value combination so that if there are L_(k) possible answers to variable k, then L_(k) “dummy” variables are created for each observation i for each clustering solution. Thus, x_(ilk) can designate a dummy variable for observation i who can answer possible value n for variable k.

At activity 5350, for each observation i and variable k, L_(k) variables can be populated per clustering solution. Thus the total number of variables created per observation per cluster is K(L_(k)). At activity 5351, if observation i gives the l-th answer for variable k, then x_(ilk) can be set to 1. At activity 5352, if observation i does not give the l-th answer for variable k, then x_(ilk) can be set to 0.

At activity 5360, all values for K(L_(k)) variables for all observations per clustering solutions can be stored. At activity 5370, for each cluster in {M}, ordinary least squares can be used to regress all Y_(im) for all observations in {R} per clustering solution. Thus regression occurs on {1,{x_(ilk):kεK,1≦l≦L_(k)−1}} so that a linear approximation can be generated to the probability that an observation with a particular set of answers to the variables in {K} can be in a particular cluster within {M}.

At activity 5380, a simpler clustering solution can be constructed to the one generated using Method 2. By “simpler” what is meant is an approximation of the actual clustering solution using the specified constraints in this case using only one variable k within {K}. At activity 5381, if weights were specified in activity 5200, an index for each observation's cluster association can be created. An index can be created by multiplying the linear approximation to the probability of an observation's cluster assignment by that cluster's specified weight. An index is created for each possible cluster assignment. At activity 5383, each observation {R} can be assigned to the m-th cluster in {M} that gives the maximized index value of the clusters in the clustering solution.

At activity 5382, if weights were not assigned in activity 5200, each observation in {R} can be assigned the m-th cluster in {M} that gives the maximum value of the linear approximation to the probabilities of being in any of the cluster members of {M} as calculated in activity 5370 for each clustering solution. At activity 5390, the outputs of regression, (i.e., the coefficients and constants) can be stored as well as the variable identifications, observation identifications, the approximated cluster assignments, and the actual cluster assignments for all clustering solutions.

Turning now to FIG. 5 c, at activity 5400, an accuracy score for the results obtained in activity 5390 for all clustering solutions can be calculated. An accuracy score can equal the number of observations, whose approximated and actual cluster assignments are identical, divided by the total # of observations in {R}. At activity 5410, the stored solution from activity 5390 that maximizes the accuracy score can be selected. At activity 5411, the outputs of regression (i.e., the coefficients and constants) can be stored along with the variable identifications, observations identifications, the approximated cluster assignments, and the actual cluster assignments for the solution that was selected in activity 5410.

At activity 5420, clustering solutions can be approximated using only 2 variables in each. At activity 5421, variable k from activity 5411 can be held constant and activities 5310 through 5400 can be executed for all possible pairs comprising k from activity 5411 and the (k+1)-th variable.

At activity 5422, the winning 2 variable solution from activity 5421 can be refined. At activity 5423, the (k+1)-th variable from activity 5421 can be held constant and activities 5310 through 5411 can be executed for all possible pairs comprising the (k+1)-th variable from activity 5421 and the remaining variables in {K}, excluding the (k+1)-th variable and the variable k identified in activity 5411. The pattern in activity 5423 can increase serially as the number of variables used to approximate the clustering solution from Method 2 increases serially.

At activity 5430, a continual loop through activities 5420 through 5423 can be performed, sequentially increasing the number of variables used in activity 5420 at the beginning of each loop until a maximum solution (in terms of accuracy) is identified for a simpler clustering solutions that uses k_(max(x)) variables to approximate the clustering solution identified in Method 2. Therefore if the (k+2)-th variable is added to the pair of the k-th and (k+1)-th variables to create a clustering solution that best approximates the objective function (i.e., the original clustering structure developed in Method 2), then in the refining activity 5422, the (k+2)-th variable is held constant while the k-th and the (k+1)-th variables are replaced with all remaining variables to test the triplet of variables that best approximates the results of Method 2.

At activity 5440, the outputs of regression, the variable identifications, the approximate cluster assignments (and the corresponding observation identifications), the actual cluster assignments (and the corresponding observation identifications), and accuracy scores for only the maximized solutions for all solutions created up through and including k_(max(x)) variables can be selected and stored. At activity 5450 the stored information from activity 5440 can be placed into a file of any appropriate format, e.g. ASCII, .txt, .csv, and/or .prn. At activity 5460 Method 5 can be terminated.

FIG. 6 a shows an exemplary embodiment of a Method 6 of present invention. All and/or part of Method 6 can also be known as the behavioral segment scoring technique.

At activity 6100, for each observation, a dataset is obtained, each dataset having variables, possible values, provided values, and corresponding cluster assignments (which could have been developed using Method 2). The dataset can consist of any combination of scalar, categorical, and/or continuous variables. At activity 6200, all continuous variables can be transformed into categorical or scalar forms. This transformation can be done by analyzing distribution boundaries within a series of ranges to find the boundaries that create as normal distributions as possible. In many situations, linear optimization is the most efficient method for performing this boundary analysis.

At activity 6300, the dataset can be refined to facilitate further analysis. At activity 6310, if the dataset has 100 or fewer variables, Method 6 can proceed to activity 6400. At activity 6320, if the data set has greater than 100 variables, the dataset can be reduced as much as possible. To perform this reduction, at activity 6321, any of the following analytical techniques can be implemented: log scores, tree analysis, regression, and/or discriminant analysis. These analytical/statistical techniques can be performed in a mathematical programming language like Fortran or using a statistical software package such as SPSS or SAS. At activity 6322, variables identified in any 3 of the 4 techniques of activity 6321 as “non-contributing” and/or “insignificant” can be removed. Although in some situations a quantity of 100 or less variables can be ideal in terms of computational efficiency, an arbitrary cut-off generally should not be forced to ensure the number of variables used in the dataset is 100 or less.

Referring to FIG. 6 b, at activity 6400, the maximum number of behavioral variables to be used in the solutions set can be specified depending on computational and/or time constraints. At activity 6410, if there are computational and/or time constraints, a maximum number of behavioral variables to be used can be selected that is less than the total number of behavioral variables in the dataset. At activity 6420, if there are no computational and/or time constraints, the maximum number of behavioral variables to be used can set equal to the total number of behavioral variables in the dataset.

At activity 6500, a determination can be made regarding whether cluster weights are needed to meet marketing coverage or marketing efficiency objectives. At activity 6510, if no weights are needed, Method 6 can proceed to activity 6600. At activity 6520, if weights are needed, each cluster can be assigned a weight using the decision rule of one of activities 6520, 6522, and 6523. In any event, if weights are needed, each cluster must be assigned a weight regardless of the cluster's magnitude. At activity 6521, if the cluster is to be unweighted, the weight can be set as w=1. At activity 6522, if the cluster is to be overweighted, the weight can be set as w>1. At activity 6523, if the cluster is to be underweighted, the weight can be set as 0<w<1.

At activity 6600, activities 5300 through 5450 of Method 5 can be executed. At activity 6700, Method 6 can be terminated.

FIG. 7 shows an exemplary embodiment of a Method 7 of the present invention. All and/or part of Method 7 can also be known as the panel analysis technique.

At activity 7100, a use for panel data is ascertained. For example, panel data can be used as an objective measure of behavior that can be input into a clustering technique such as that of Method 2. As another example, panel data can be used for post-clustering analyses, e.g. tracking, promotion, media performance, or positioning.

At activity 7110, if the panel data is to be used as objective measure, then at activity 7111, the data collection instrument, e.g. survey, can be refined using Method 1 (i.e., the Babbitt Score technique). At activity 7112, the data collection instrument can be fielded within a panel (e.g. IRI, Nielsen, Simmons, comSCORE and/or MRI). At activity 7113, observations can be extracted from the collected data to assemble a dataset that reflects a category's and/or industry's underlying demographics. In some situations, this activity can be necessary to ensure that the clustering solution developed using this dataset is truly representative of a market or industry and is not just the function of an idiosyncratic group of the overall population.

At activity 7114, the dataset can be obtained for the extracted observations, the dataset having variables, possible values, and provided values. At activity 7115, the panel-based behavioral variables can be appended to each corresponding observation in the dataset. At activity 7116, any panel variables that are continuous can be transformed into categorical or scalar variables. This transformation can be performed by analyzing distribution boundaries within a series of ranges to find the boundaries that create as normal distribution as possible. In many situations, linear optimization is the most efficient method for executing this analysis.

At activity 7117, the dataset from activity 7116 can be input into activity 2420 of Method 2 (the Bestfit Clustering technique) and clustering can proceed using Method 2. At activity 7118 the process can terminate.

Turning now to use of the panel data for post-clustering analysis, at activity 7121, data is collected. At activity 7121.1, if the dataset was developed using the panel data as an objective measure of behavior, Method 7 can continue to activity 7122, where Methods 2 through 4 can be executed.

Alternatively, if panel data was used for post-clustering analysis, Method 7 can continue to activity 7121.2, where the data collection instrument can be refined using Method 1 (the Babbitt Score technique). At activity 7123, Methods 2 through 5 can be executed. At activity 7123.1, the typing tool developed in activity 7123 can be used to cluster score a representative sample of the panel's members using an expedient contact channel (e.g., outbound telephone, e-mail/electronic surveys, and/or mail-based surveys, etc.). At activity 7124, a cluster level analysis can be executed using the panel data. At activity 7125, Method 7 can be terminated.

FIG. 8 is a flowchart of an exemplary embodiment of a Method 8 of the present invention. All and/or part of Method 8 can also be known as the overall segment-based marketing methodology, and can include some or all of Methods 1 through 10.

At activity 8100, a pilot survey can be developed and fielded. At activity 8200, the survey can be refined. This refinement can implement Method 1 and/or Method 2.

At activity 8300, a full survey can be fielded. At activity 8400, the data from the survey can be cleaned, refined, and otherwise analyzed, using, for example, Method 4. At activity 8500, clusters can be created using, for example, Method 1, 3, and/or 7. At activity 8600, clusters can be refined using, for example, Method 4.

At activity 8700, Method 5 can be implemented. At activity 8750, Method 6 can be used. At activity 8800, panel variables can be appended to the results of activity 8750, and Method 7 can then be utilized in activity 8850. At activity 8900, insights into the segments can be gained, and at activity 8950, marketing ideas, strategies, and tactics can be developed and implemented.

FIG. 9 provides a block diagram of an embodiment of an information device 9 of the present invention. As an initial matter, it suffices to say that, using the description of methods 1 through 8 and 10, one of ordinary skill in the art can implement the functionality of methods 1 through 8 and 10 via information device 9 utilizing any of a wide variety of well-known architectures, hardware, protocols, and/or software. Thus, the following description of information device 9 can be viewed as illustrative, and should not be construed to limit the implementation of methods 1 through 8 or 10.

Information device 9 can include well-known components such as one or more processors 9120, one or more memories 9140 containing instructions 9160, one or more input/output (I/O) devices 9180, and one or more network interfaces 9190.

In one embodiment of information device 9, each processor 9120 can be a general purpose microprocessor, such a the Pentium series microprocessor manufactured by the Intel Corporation of Santa Clara, Calif. In another embodiment, the processor can be an Application Specific Integrated Circuit (ASIC) which has been designed to implement in its hardware and/or firmware at least a part of a method in accordance with an embodiment of the present invention.

Any memory 9140 can be coupled to a processor 9120 and can store instructions 9160 adapted to be executed by processor 9120 according to one or more actions of methods 1 through 9. Memory 9140 can be any device capable of storing analog or digital information, such as a hard disk, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, a compact disk, a magnetic tape, a floppy disk, and any combination thereof.

Instructions 9160 can be embodied in software, which can take any of numerous forms that are well-known in the art. For example, information device 9 can access one or more databases having a flat file or a relational organization, and a centralized or distributed architecture. For instance, those of skill in the art can tailor items such as an SQL database to provide the functionality of methods 1 through 8 and 10. One supplier of such database items can be Oracle Corporation, of Redwood Shores, Calif. Moreover, software tools such as EDI, FTP, HTTP, HTML, XML, cXML, XSL, and WAP can be utilized for communications between information devices. Additionally, information device 9 can utilize platform-independent and/or network-centric software tools such as, for example, Java or JavaScript.

Any input/output (I/O) device 9180 can be an audio and/or visual device, including, for example, a monitor, display, keyboard, keypad, touchpad, pointing device, microphone, speaker, video camera, camera, scanner, and/or printer, including a port to which an I/O device can be attached or connected.

Any network interface 9190 can be a telephone, a traditional data modem, a fax modem, a cable modem, a digital subscriber line interface, a bridge, a hub, a router, or other similar devices.

FIG. 10 is flowchart of an exemplary embodiment of a method 10 of the present invention. Method 10 is also known as the fitness score calculation technique. At activity 10010, modes of given values for all variables in {K} for cluster n are calculated, where n is an element of {N}, which is an element of {I}, and N consists of a finite, non-zero, positive number of clusters and I is the domain of integers. Also, k is an element of {K}, which is an element of {I}, and K consists of a finite, non-zero, positive number of clusters and I is the domain of integers.

At activity 10020, the modes, their corresponding variable identifications, and their corresponding cluster identifications are stored. At activity 10030, modes of given values for all variables in {K} for cluster n+y are calculated. At activity 10040, the modes, their corresponding variable identifications, and their corresponding cluster identifications are stored. At activity 10050, an assessment is made regarding the number of clusters for which modes have been calculated. At activity 10060, if the number of clusters for which modes have been calculated is equal to N, Method 10 proceeds to activity 10080. At activity 10070, if the number of clusters for which modes have been calculated is less than N, Method 10 returns to activity 10030.

At activity 10080, for each cluster, the value provided by each constituent member for variable k is compared to the cluster's mode for variable k. At activity 10090, the value of i_(n) for k is compared to the mode of k_(n), where i is an element of {R}, which is an element of {I}, and R consists of the set of observations, whose total number of constituents is greater than 0, and I is the domain of integers, and i_(n) is a member of cluster n, and k_(n) is the variable k as answered by cluster n.

At activity 10100, if the value of in for k is equal to the mode of k_(n), V_(kni) is set to 1, where V_(kni) is the score for an observation i that is in cluster n and has provided answer value V for variable k. At activity 10110, if the value of in for k is not equal to the mode of k_(n), V_(kni) is set to zero.

At activity 10120, V_(kni) is stored. At activity 10130, V_(kni) can be adjusted by an indicated weight. At activity 10140, if a weight was specified, V_(kni) is multiplied by the corresponding weight for k. At activity 10150, if a weight was not specified, V_(kni) is multiplied by 1. At activity 10160, V_(kni) is stored.

At activity 10170, activities 10080 to 10160 are repeated until a score of V is calculated for all observations in their respective clusters for all variables k. At activity 10200, all scores V are summed for all observations across all variables to arrive at the fitness score. At activity 10300, the fitness is stored. At activity 10400, method 10 is terminated.

ADDITIONAL EMBODIMENTS

Still other advantages of the present invention will become readily apparent to those skilled in this art from the above-recited detailed description. Accordingly, the drawings and descriptions are to be regarded as illustrative in nature, and not as restrictive. For example, embodiments of Methods 1, 2, 3, 4, 5, 6, 7, 8 and/or 10 of the present invention can be viewed as germane to a number of disparate disciplines beyond business and marketing. The following list outlines examples of problems in these fields that can be solved by applying one or more of the aforementioned methods.

-   -   1. Evolutionary Biology         -   a. Illustrative problem(s): Taxonomy is a critical component             of demonstrating biological convergence/divergence. However,             this process often involves subjective comparisons of             fossils and dissection results.         -   b. New/improved solution(s): Embodiments of the inventions             disclosed herein can be adapted to group observations of             living (not currently alive but to distinguish from             non-living things such as rocks or stars) specimens by using             images (which are in essence transformed into numerical             sequences) of the living things. This clustering will             improve biologists' ability to understand which organisms             were converging and diverging from one another.     -   2. Molecular Biology         -   a. Illustrative problem(s): Understanding the biological             impact of particular proteins, enzymes, genes, and other             microscopic (and usually sub-cellular) entities is a             time-intensive process.         -   b. New/improved solution(s): Biologists will be able to             leverage libraries of previously identified microscopic             entities with known functions/properties in order to create             high-resolution “clusters” that can be transformed into             biological typing tools for rapidly classifying and             describing novel microscopic entities.     -   3. Finance         -   a. Illustrative problem(s): Understanding stock market             volatility is a largely “intuitive” process; attempts at             “quantitative trading” have largely ended in disaster (stock             market collapses, failure of firms engaging in that             activity)         -   b. New/improved solution(s): Better understanding of how             different industries' stock prices respond to differing             market pressures; ability to analyze historical data to             assemble portfolios (i.e. clusters of different stocks)             optimized against a particular objective     -   4. Economics         -   a. Illustrative problem(s): Regression analysis is useful             for isolating factors that contribute to the occurrence of a             phenomenon, such as changes in CPI or GDP, but is less             useful for understanding macro-economic issues such whether             a nation is eligible for membership to an economic group,             such as Turkey into the European Union         -   b. New/improved solution(s): Large-scale, high-resolution             clustering and composition analysis would enable economists             and policy-makers to develop quantitative decision rules for             governing macro-economic dynamics     -   5. Politics         -   a. Illustrative problem(s): Contacting a household not             affiliated with a particular political party is a waste of             time and resources; how to optimize media spending to             enhance CPM on a political-affiliation basis; how to             effectively identify centrists/undecided voters and             communicate with them         -   b. New/improved solution(s): Superior method for             understanding polling data via high resolution clustering;             ability to predict political affiliation using unobtrusive             questions; ability to score media vehicles to determine             which are more widely viewed by a particular constituency     -   6. Psychology         -   a. Illustrative problem(s): Executing psychology experiments             on a large scale (n≧5,000) in a normalized population is             difficult without introducing too many biases into the study         -   b. New/improved solution(s): Because most psychology studies             involve measuring responses to stimuli, the inventors'             inventions give psychologists the ability to conduct a             comprehensive study on a smaller scale population and             develop efficient “field tests” that only use the most             predictive questions from the comprehensive study     -   7. Sociology         -   a. Illustrative problem(s): Executing sociology studies on a             large scale (n≧5,000) in a normalized population is             difficult without introducing too many biases into the study         -   b. New/improved solution(s): Because most sociology studies             involve qualitative surveys, sociologists can conduct a             comprehensive study on a smaller scale population and             develop efficient “field tests” that only use the most             predictive questions from the comprehensive study     -   8. Chemistry/Chemical engineering         -   a. Illustrative problem(s): One of the most time-consuming             aspects of chemistry research (e.g. pharmaceuticals,             industrial chemicals) is determining if a newly-formed             compound is chemically relevant. For example, the biggest             rate-limiting step in the pharmaceutical research process             (before clinical trials begin) is sorting out biologically             relevant compounds from the ones that are toxic or otherwise             devoid of practical application in mammalian systems.         -   b. New/improved solution(s): Leverage libraries of             previously identified chemicals with useful properties in             order to create high-resolution “clusters” that can be             transformed into chemical typing tools for rapidly             classifying and describing novel chemicals.     -   9. Pharmaceutical drug development         -   a. Illustrative problem(s): A difficult, time consuming, and             expensive part of the drug development process is conducting             clinical trials because of the difficulty in pinpointing             stable populations for which the drug in question can be             indicated. Failure to prove that a specific group of people             exists for whom a drug can be relevant will cause the Food             and Drug Administration to deny approval of that drug. This             group must be a stable population that is statistically             relevant.         -   b. New/improved solution(s): By using inventors' Bestfit             clustering invention, pharmaceutical companies will be able             to rapidly identify stable, statistically relevant             populations for whom the drug in question is relevant. The             inventors' Segment-on-the-Fly invention will allow             pharmaceutical companies to accurately find members of the             target population for clinical testing.     -   10. Astrophysics/Astronomy         -   a. Illustrative problem(s): Neural nets are currently used             to describe/classify newly discovered heavenly bodies.             Unfortunately, neural networks are blackbox systems that             cannot be modified once they initiate analysis.         -   b. New/improved solution(s): Because the inventors'             inventions are not neural networks, they can accomplish the             same objectives in faster cycle times with the added             flexibility of adding/removing data mid-process.             Furthermore, astronomical typing tools for different classes             of space-borne objects can be created to accelerate             identification cycles in the field.     -   11. Linguistics         -   a. Illustrative problem(s): Historical linguistics employs             basic statistical tools and “logic-based intuition” to             understand the evolution (both convergence and divergence)             of different language groups; unfortunately linguists have             not been able to definitely answer pressing questions such             as whether or not genetic relationships among languages             (e.g. hypothesis of Japanese as an Altaic language) are             actual or serendipitous.         -   b. New/improved solution(s): Because the essence of             linguistics is discovering patterns/clusters of             grammar/syntax/lexicon and understanding distances among             groups of languages, the inventors' innovations in             clustering and composition analysis are well-suited to             quantitatively proving genetic relationships among             languages.     -   12. Medicine         -   a. Illustrative problem(s): Although medicine is a blend of             art and science, there is an over-reliance on intuition and             experience in diagnosing patients. Consequently, there is             substantial room for initial misdiagnoses, which result in             lost time and compromised treatment.         -   b. New/improved solution(s): Because much of medicine relies             upon case histories and experience, the inventors'             inventions can be easily adapted to create segmentation             structures of different therapeutic areas and develop             diagnostic “disease/ailment typing tools” for use in patient             interviews. The inventors screener would help to narrow the             number of possibilities doctors would need to consider when             examining a patient.     -   13. Anthropology         -   a. Illustrative problem(s): Like its social sciences             brethren, anthropology has historically relied upon largely             a priori interpretations of observed data, in this case             human behaviors/cultural phenomena.         -   b. New/improved solution(s): By using the inventors'             inventions to create high-resolution clustering structures             of recorded human behaviors/cultural phenomena,             anthropologists can quantitatively establish the             similarity/dissimilarity of sundry human societies and trace             convergence/divergence in socialization patterns.             This list is by no means exhaustive (in overall scope of             applicable disciplines nor in the applications to specific             disciplines) but is meant to demonstrate the potential             universality of embodiments of Methods 1, 2, 3, 4, 5, 6, 7,             8 and/or 10. 

1. A computer-assisted method for evaluating a cluster assignment for an observation, comprising the activities of: for each of a plurality of observations, obtaining a data set containing no more than one proxy value for each of a plurality of variables, each variable having a plurality of possible values, the data set also containing a cluster assignment for the observation, the cluster assignment identifying one cluster from a plurality of clusters; for each observation from the plurality of observations, calculating a percent of proxy values for the plurality of variables that equals a mode of that observation's corresponding cluster's proxy values for the corresponding variables; and automatically assigning a human respondent associated with a determined observation to a cluster responsive to a determination that a value of a variable provided by the human respondent causes the human respondent to be classified as typical of the cluster based upon the percent for at least one observation, one or more of the plurality of cluster usable to manage a marketing strategy.
 2. A computer-assisted method for evaluating a cluster assignment for an observation, comprising the activities of: for each of a plurality of observations, obtaining a data set containing no more than one proxy value for each of a plurality of variables, each variable having a plurality of possible values, the data set also containing a cluster assignment for the observation; for each observation from the plurality of observations, estimating a purposeful probability that a particular possible value from the plurality of possible values for a particular variable will be purposefully provided by observations assigned to a particular cluster from a plurality of clusters; and automatically assigning a human respondent associated with a determined observation to a second cluster of the plurality of clusters responsive to a determination that a value of a variable provided by the human respondent causes the human respondent to be classified as an outlier of a first cluster of the plurality of clusters based upon at least one purposeful probability, one or more of the plurality of clusters usable to manage a business tactic.
 3. The method of claim 1, further comprising the activities of: for each observation from the plurality of observations in each cluster from the plurality of clusters, calculating a serendipity probability for each possible value, the serendipity probability is a measure of a probability that an observation in a particular cluster will be randomly associated with any one of the plurality of possible values for a particular variable; for each observation from the plurality of observations, calculating a ratio of the purposeful probability to the serendipity probability; for each observation from the plurality of observations, calculating a logarithm of the ratio to obtain composition analysis score; and outputting the composition analysis scores for each observation in each cluster.
 4. The method of claim 1, further comprising the activities of: for each observation from the plurality of observations, assuming that before the observation can be made, the observation has an equal probability of being in any identified cluster from the plurality of clusters; for each observation from the plurality of observations, assuming that the purposeful probabilities are true; for each observation from the plurality of observations, using Bayes' Theorem to calculate a Bayes probability that a particular observation can be in each cluster conditional upon the observation's proxy value to each variable; outputting the Bayes probability that each observation can be in each cluster.
 5. A computer-readable medium containing instructions for activities comprising: for each of a plurality of observations, obtaining a data set containing no more than one proxy value for each of a plurality of variables, each variable having a plurality of possible values, the data set also containing a cluster assignment for the observation, the cluster assignment identifying one cluster from a plurality of clusters; for each observation from the plurality of observations, calculating a percent of proxy values for the plurality of variables that equals a mode of that observation's corresponding cluster's proxy values for the corresponding variables; and automatically assigning a determined observation, of the plurality of observations, to a second cluster of the plurality of clusters responsive to a determination that a value of a variable causes the determined observation to be classified as between a first cluster of the plurality of clusters and the second cluster based upon an output of the percent for the observation, one or more of the plurality of clusters usable to make a medical diagnosis.
 6. An apparatus for evaluating a cluster assignment for an observation, comprising: for each of a plurality of observations, means for obtaining a data set containing no more than one proxy value for each of a plurality of variables, each variable having a plurality of possible values, the data set also containing a cluster assignment for the observation, the cluster assignment identifying one cluster from a plurality of clusters; for each observation from the plurality of observations, means for calculating a percent of proxy values for the plurality of variables that equals a mode of that observation's corresponding cluster's proxy values for the corresponding variables; and a processor configured to automatically assign a determined observation, of the plurality of observations, to a cluster responsive to a determination that a fraction of values of variables associated with the determined observation correspond to values typical of the cluster based upon an output of the percent for the determined observation, one or more of the plurality of clusters usable to manage a financial strategy.
 7. A computer-readable medium containing instructions for activities comprising: for each of a plurality of observations, obtaining a data set containing no more than one proxy value for each of a plurality of variables, each variable having a plurality of possible values, the data set also containing a cluster assignment for the observation; for each observation from the plurality of observations, estimating a purposeful probability that a particular possible value from the plurality of possible values for a particular variable will be purposefully provided by observations assigned to a particular cluster from a plurality of clusters; and automatically assigning a determined observation of the plurality of observations, to a second cluster of the plurality of clusters responsive to a determination that a fraction of a values of variables associated with the determined observation causes the determined observation to be classified as an outlier of a first cluster of the plurality of clusters based upon an output of at least one purposeful probability, one or more of the plurality of clusters usable to manage a pharmaceutical drug development process.
 8. An apparatus for evaluating a cluster assignment for an observation, comprising: for each of a plurality of observations, means for obtaining a data set containing no more than one proxy value for each of a plurality of variables, each variable having a plurality of possible values, the data set also containing a cluster assignment for the observation; for each observation from the plurality of observations, means for estimating a purposeful probability that a particular possible value from the plurality of possible values for a particular variable will be purposefully provided by observations assigned to a particular cluster from a plurality of clusters; and a processor configured to automatically assign a determined observation to a second cluster of the plurality of clusters responsive to a determination that a fraction of a values of variables associated with the determined observation causes the determined observation to be classified as between a first cluster of the plurality of clusters and the second cluster based upon at least one purposeful probability, one or more of the plurality of clusters usable to make an economic decision. 